Abstract 4354328: What’s On Shelf Shapes What’s in Heart: Retail Food Environment and County-Level Cardiac Mortality in the United States, A Nationwide Ecological Study

BackgroundOutside of the United States, evidence for associations between exposure to fast-food establishments and risk for obesity among adults is limited and equivocal. The purposes of this study were to investigate whether the relative availability of different types of food retailers around people's homes was associated with obesity among adults in Edmonton, Canada, and if this association varied as a function of distance between food locations and people's homes.MethodsData from a population health survey of 2900 adults (18 years or older) conducted in 2002 was linked with geographic measures of access to food retailers. Based upon a ratio of the number of fast-food restaurants and convenience stores to supermarkets and specialty food stores, a Retail Food Environment Index (RFEI) was calculated for 800 m and 1600 m buffers around people's homes. In a series of logistic regressions, associations between the RFEI and the level of obesity among adults were examined.ResultsThe median RFEI for adults in Edmonton was 4.00 within an 800 m buffer around their residence and 6.46 within a 1600 m buffer around their residence. Approximately 14% of the respondents were classified as being obese. The odds of a resident being obese were significantly lower (OR = 0.75, 95%CI 0.59 – 0.95) if they lived in an area with the lowest RFEI (below 3.0) in comparison to the highest RFEI (5.0 and above). These associations existed regardless of the covariates included in the model. No significant associations were observed between RFEI within a 1600 m buffer of the home and obesity.ConclusionThe lower the ratio of fast-food restaurants and convenience stores to grocery stores and produce vendors near people's homes, the lower the odds of being obese. Thus the proximity of the obesogenic environment to individuals appears to be an important factor in their risk for obesity.

The Retail Food Environment Index (RFEI) and its variants have been widely used in public health to measure people’s accessibility to healthy food. These indices are purely environmental as they only concern the geographic distribution of food retailers, but fail to include human factors, such as demographics, socio-economy, and mobility, which also shape the food environment. The exclusion of human factors limits the explanatory power of RFEIs in identifying neighborhoods of the greatest concern. In this study, we first proposed a hybrid approach to integrate human and environmental factors into the RFEI. We then demonstrated this approach by incorporating neighborhood commuting patterns into a traditional RFEI: we devised a multi-origin RFEI (MO_RFEI) that allows people to access food from both homes and workplaces, and further an enhanced RFEI (eRFEI) that allows people to access food with different transportation modes. We compared the traditional and proposed RFEIs in a case study of Florida, USA, and found that the eRFEI identified fewer and more clustered underserved populations, allowing policymakers to intervene more effectively. The eRFEI depicts more realistic human shopping behaviors and better represents the food environment. Our study enriches the literature by offering a new and generic approach for assimilating a neighborhood context into food environment measures.

The aim of this study was to examine the association between the local food environment and obesity proportions among 3- to 4-year-old children who were participants in the WIC program in Los Angeles County using spatial analyses techniques. ArcGIS, spatial analysis software, was used to compute the retail food environment index (RFEI) per ZIP code. GeoDa, spatial statistics software was employed to check for spatial autocorrelation and to control for permeability of the boundaries. Linear regression and ANOVA were used to examine the impact of the food environment on childhood obesity. Fast-food restaurants represented 30% and convenience stores represented 40% of the sum of food outlets in areas where WIC participants reside. Although there was no statistically significant association between RFEI and 3- to 4-year-old obesity proportions among WIC children, analysis of variance (ANOVA) tests demonstrated statistically significant positive associations between obesity and the number of convenience stores and the number of supermarkets. Our findings suggest that RFEI, as currently constructed, may not be the optimal way to capture the food environment. This study suggests that convenience stores and supermarkets are a likely source of excess calories for children in low-income households. Given the ubiquity of convenience stores in low-income neighborhoods, interventions to improve availability of healthy food in these stores should be part of the many approaches to addressing childhood obesity. This study adds to the literature by examining the validity of the RFEI and by demonstrating the need and illustrating the use of spatial analyses, using GeoDA, in the environment/obesity studies.

Poor food environments are considered to trigger obesity and related health complications by restricting the local food options to predominantly low quality, energy-dense foods. This study investigated the impact of the food environment on obesity with a focus on any changes that might have occurred around the COVID lockdown period in the UK when majority of the population relied on food delivery and the local food environments. The proportion of fast-food retailers in the area and the Retail Food Environment Index (RFEI) were calculated for participants of the 1970 British Cohort Study (BCS70) at three timepoints: pre-COVID (2016), the first UK nation-wide lockdown (April–May 2020) and post lockdown (September–October 2020). The association of the food environment and the odds of obesity was estimated through multivariable logistic regression, with adjustments being made for selected socioeconomic variables. A model using the fast-food proportion as the sole predictor estimated that higher fast-food proportion increased the odds of obesity by 2.41 in 2016, 2.89 during the lockdown and 1.34 post lockdown, compared with 1.87, 2.23, and 0.73, respectively, for the same three periods with adjustments being made for select socioeconomic variables. On the other hand, RFEI increased the odds of obesity only slightly at 1.01, 1.02 and 1.03, respectively, with the model with adjustments yielding respective similar values. The fast-food proportion model indicates that proximity to a poor food environment is linked to obesity, especially during the COVID lockdown period, but the impact of a poor-food environment is limited if the RFEI is used as its indicator. The findings will add much needed insights on the UK data and will inform public health planning and policy.

Background Obesity is an important and highly prevalent risk factor for non-communicable diseases in both developed and developing countries. Obesity prevalence is influenced by a complex, multifaceted system of determinants among which the food retailing and advertising environment is pivotal. Current food environments are often characterised by pervasive exposure to unprecedented availability and marketing of energy-rich and nutrient-poor foods. Mexico has one of the highest obesity rates in the world: 70% of the population is overweight or obese. The country has experienced a dietary and food retail transition involving increased high-calorie-dense food and drink availability. The aims of this study were 1) to analyse the associations between total food outlet density and BMI; 2) to examine the association of the retail food environment index (RFEI) and obesity; and 3) to study the association of the density of individual food outlets and obesity in Mexican adults in urban areas. Methods The National Institute of Statistics and Geography in Mexico provided geographical and food outlet data; BMI, calculated from anthropometric measurements, and socio-economic characteristics of a nationally-representative sample of adults aged 18+, came from participants in the National Health and Nutrition Survey in Mexico (ENSANUT) 2012. I calculated densities of supermarkets, restaurants, chain and non-chain convenience stores, and fruit and vegetable stores in total and by individual type per 1000 people per census tract area, using ArcGIS. I calculated RFEI, the ratio of ‘unhealthy’ to ‘healthy’ food outlets. Using multilevel linear regression, I analysed the relationship between density of food outlet types and obesity using complex survey design in STATA14. All analyses were adjusted for sex, age, socioeconomic status and physical activity. Results Both non-chain convenience store density [β=3.10, 95% CI 0.97 to 5.23, p=0.004] and non-chain combined with chain-type convenience store density [β=2.71, 95% CI 0.63 to 4.80, p=0.011] were significantly associated with obesity. Total food outlet density showed no significant association with obesity. However, the RFEI was associated with higher levels of obesity [β=0.040, 95% CI 0.00049 to 0.02, p=0.040]. Conclusion Convenience stores, which offer a greater availability of energy dense foods with low nutrient content, pose a risk to higher levels of obesity. A balance of healthier food outlets versus non-healthy food outlets could decrease the risk of obesity in urban areas of Mexico.

Outside of western countries, the study of the local food environment and evidence for its association with dietary behavior is limited. The aim of this paper was to examine the association between the local retail food environment and consumption of fruit and vegetables (FV) among adults in Hong Kong. Local retail food environment was measured by density of different types of retail food outlets (grocery stores, convenience stores, and fast food restaurants) within a 1000 m Euclidean buffer around individual’s homes using a geographic information system (GIS). The Retail Food Environment Index (RFEI) was calculated based on the relative density of fast-food restaurants and convenience stores to grocery stores. Logistic regressions were performed to examine associations using cross-sectional data of 1977 adults (18 years or older). Overall, people living in an area with the highest RFEI (Q4, >5.76) had significantly greater odds of infrequent FV consumption (<7 days/week) after covariates adjustment (infrequent fruit consumption: OR = 1.36, 95% CI 1.04–1.78; infrequent vegetable consumption: OR = 1.72, 95% CI 1.11–2.68) in comparison to the lowest RFEI (Q1, <2.25). Highest density of fast food restaurants (Q4, >53) was also significantly associated with greater odds of infrequent fruit consumption (<7 days/week) (unadjusted model: OR = 1.34, 95% CI 1.04–1.73), relative to lowest density of fast food restaurants (Q1, <13). No significant association of density of grocery stores or convenience stores was observed with infrequent FV consumption regardless of the covariates included in the model. Our results suggest that the ratio of fast-food restaurants and convenience stores to grocery stores near people’s home is an important environmental factor in meeting fruit and vegetable consumption guidelines. “Food swamps” (areas with an abundance of unhealthy foods) rather than “food deserts” (areas where there is limited access to healthy foods) seems to be more of a problem in Hong Kong’s urban areas. We advanced international literature by providing evidence in a non-western setting.

Weight Loss Maintainers Sustain High Diet Quality in Diverse Residential Retail Food Environments

High prevalence of obesity and non-communicable diseases is a global public health problem, in which the quality of food environments is thought to play an important role. Current scientific evidence is not consistent regarding the impact of food environments on diet. The relationship between local food environments and diet quality was assessed across 10 Australian suburbs, using Australian-based indices devised to measure the two parameters. Data of dietary habits from the participants was gathered using a short questionnaire. The suburbs’ Food Environment Score (higher being healthier) was associated with higher consumption of fruit (χ2 (40, 230) = 58.8, p = 0.04), and vegetables (χ2 (40, 230) = 81.3, p = 0.03). The Food Environment Score identified a significant positive correlation with four of the diet scores: individual total diet score (rs = 0.30, p < 0.01), fruit and vegetable score (rs = 0.43, p < 0.01), sugary drink score (rs = 0.13, p < 0.05), and discretionary food score (rs = 0.15, p < 0.05). Moreover, the suburbs’ RFEI (Retail Food Environment Index, higher being unhealthier) showed a significant association with higher consumption of salty snacks (χ2 (24, 230) = 43.9, p = 0.04). Food environments dominated by food outlets considered as ‘healthier’ were associated with healthier population food intakes, as indicated by a higher consumption of fruit, vegetables, and water, as well as a lower consumption of junk food, salty snacks, and sugary drinks. This association suggests that healthier diet quality is associated with healthier food environments in regional Australia.

In Mexico, 75% of the population are affected by overweight or obesity, and the availability and affordability of high-calorie-dense foods and beverages are high. This study tested the association between the retail food environment index (RFEI), dietary patterns, body mass index (BMI), and socioeconomic position (SEP) in Mexico. Cross-sectional diet, health, and sociodemographic population-based secondary data analyses were conducted. The RFEI was calculated by dividing the total number of fast-food outlets and convenience stores by the total number of supermarkets and fruit and vegetable stores per census tract area. Associations between BMI, dietary patterns, SEP and the RFEI were tested using multilevel linear regression, including interactions of the RFEI with SEP, gender, and age. Living in neighbourhoods with a higher RFEI was associated with a 0.01kg/m2 higher BMI (β = 0.01, 95%CI: 0.0005, 0.02, p = 0.04), equivalent to a mean 0.046 weight gain for a 1.60m tall person per 10% higher RFEI. Unhealthy dietary patterns were more likely in neighbourhoods with a higher RFEI (β = 0.100, 95%CI: 0.03, 0.12, p = 0.001). Multilevel linear regression showed that lower SEP households had a higher RFEI compared to higher SEP households (β = 0.020, 95% CI: -0.006 to 0.04, p = 0.10). Generalised structural equation models revealed a graded relationship between RFEI and SEP, showing that lower SEP households were exposed to a higher RFEI (β = 0.060, 95% CI: 0.05 to 0.07, p < 0.001.) The study identified significant associations between higher proportions of fast-food outlets and convenience stores, higher BMI, and unhealthy dietary patterns. It was particularly evident that low-income populations are more likely to be exposed to obesogenic food environments.

Introduction: The role of “food swamps”, an area characterized by a high-density of establishments selling fast-food and junk food relative to healthier options, on incident stroke is not well studied. Hypothesis We hypothesized that a higher retail food environment index (RFEI), indicative of food swamps, would be associated with greater odds of incident stroke. Methods: The sample comprised of community-dwelling stroke-free participants aged ≥50 years who enrolled in the 2010 epoch of the Health and Retirement Study (HRS), which is representative of the US population. If a participant moved to a new area during follow-up through 2016, we only considered incident strokes reported before relocation. The traditional RFEI is a county’s ratio of the number of fast-food restaurants and convenience stores to the number of grocers. The expanded RFEI additionally includes full-service restaurants as unhealthy food options, and farmers’ markets and specialized food stores as healthy food retailers. We averaged RFEI across all included follow-up years and dichotomized RFEI using a threshold of 5, previously shown to discriminate obesity rates. We used logistic regression models to assess the association between RFEI groups and incident stroke, adjusting for key covariates and weighting to account for survey design. Results: Among 84,023,542 participants (mean age 64±10 years, 54% female, 84% white) in weighted analysis, 3,224,378 (3.8%) reported an incident stroke during follow-up. The average traditional and expanded RFEI were 6.5±2.7 (72% in ≥5 group) and 6.9±2.3 (84% in ≥5 group), respectively. In fully adjusted weighted analyses, the higher traditional RFEI group had greater odds of incident stroke compared to the lower group (OR [95% CI]: 1.135 [1.132-1.138]). We found a similar association with expanded RFEI groups and incident stroke (OR [95% CI]: 1.095 [1.092-1.098]). Conclusions: Among community-dwelling adults in HRS, RFEI was associated with incident stroke, independent of demographics and health characteristics. Results highlight the potential importance of an area’s food options as a structural determinant in stroke, especially given most participants resided in areas with 6 times the amount of relative unhealthy to healthy food choices.

BackgroundObesity is a worldwide public health issue. Many factors contribute to this: a holistic approach is required to tackle it. Due to the rapid increase in obesity in many parts...

Development of accurate and sensitive methods to characterize the food environment is needed. Thus, we examined convergent and criterion validity of 2 retail food environment data sources and then examined differences in predictive validity between 3 ways of measuring the rural and urban food environment. Ten counties were selected in each of 3 North Carolina regions (n = 30). Number of fast-food restaurants and chain supermarkets were calculated using 2 data sources. Convergent validity was percent agreement between the 2 sources. Criterion validity was percent agreement between each source and the most accurate venue count. Predictive validity of food environment measures (Retail Food Environment Index, fast-food restaurants/capita, and supermarkets/capita) was calculated by associations with county-level mean-weighted body mass index (BMI). Percent agreement for fast-food restaurants ranged from 50% to 100% (mean = 87%) and for supermarkets ranged from 58% to 100% (mean = 89%). The 2 data sources had similar percent agreement with the most accurate count. Retail Food Environment Index was positively associated with BMI, while fast-food restaurants per capita were negatively associated with BMI. Our results lend support to studies using both food environment data sources examined.

Objective Food Environments and Health Outcomes

Urban Food Environments and Residents’ Shopping Behaviors

"Food swamps" are areas with a high density of fast-food restaurants (unhealthy foods) relative to grocery stores (healthy foods). Patients in regions with inequitable access to healthy foods may have worsened surgical outcomes due to suboptimal nutrition. The present study assesses complication rates in breast reduction patients residing in regions with high food swamp scores (FSSs). An institutional retrospective review of patients who underwent breast reduction surgery between 2015 and 2023 was conducted. The USDA Food Environment Atlas was accessed to identify New York county-level data. The Retail Food Environment Index was used to calculate all county FSSs and categorized as low, moderate, and high. Regression analysis assessed FSS as a predictor of complications. A total of 1052 patients (1965 breasts) were identified and resided in low (22.1%), moderate (39.8%), or high (38%) FSS counties. Hispanic patients were predominant in high FSS counties (37.8%, P < 0.001). The overall complication rate was 11.3% (n = 119), with an increased rate in the high versus low FSS cohort (43.7% vs 14.3%, P = 0.039). Unplanned reoperations occurred in 31 patients (18.2%), more frequently in the high FSS cohort (4.3% vs 0.9%, P = 0.016). Regression analysis demonstrated a higher odds ratio (OR) for overall postoperative complications (OR, 1.9, P = 0.028), infections (OR, 1.34, P = 0.04) and unplanned reoperations (OR, 5.13, P = 0.03) among high FSS counties. Environmental aspects of food swamps may increase complication risks following breast reduction. The present findings reinforce the need for further research on the interplay between food environmental factors and breast reduction outcomes.